Biological systems are extremely complex and new tools are necessary to effectively enhance our knowledge and understanding of their underlying mechanisms. Mass spectrometry has proven to be an essential technology for the analysis of biological samples due to its unparalleled throughput, sensitivity, and specificity. IMS combines the power of MS with the inherent spatial information contained in a biological sample revealing biomolecular distributions on a scale unattainable by other techniques.

The work presented here has two main focuses. First, chapter 2 details the development of a method to carry out in situ chemical reactions to enhance the proteomic information that can be detected in a tissue sample. This method, referred to as in situ digestion, uses an enzyme to digest the proteins present in discrete regions of a tissue prior to matrix application and MS analysis. The enzymatically digested proteins result in a large collection of proteolytic peptides in the range of m/z 700-3000. This collection of peptides can be sequenced and identified directly from the tissue using tandem MS and then linked back to their respective intact protein. The spatial information for these ions can be used to validate their identification because all peptides from the same protein should exhibit the same spatial distribution. In situ digestion also enables the indirect detection of high molecular weight proteins (> m/z 30,000) that are not detected in a standard protein imaging experiment by mapping the peptides generated from these species. Finally, this method enables the analysis of formalin-fixed paraffin-embedded (FFPE) tissues. FFPE tissues are a valuable and extensive source of clinical samples which are not compatible for use in standard protein imaging experiments.

Second, chapters 3 and 4 detail the application of this method to FFPE lung cancer tissue microarrays (TMA). We show that in situ digestion coupled with imaging mass spectrometry can be used to reproducibly map numerous protein species in a large population of lung cancer patients. Several of these proteins correspond to previously reported markers of lung cancer, while others have not yet been associated with this disease. Furthermore, we discovered patterns of protein expression that can be used to accurately distinguish and classify the histological subtypes present in the sample cohort.

In summary, this work shows that when IMS is performed on a large set of tissues in parallel with a pathological evaluation, the combined histological and molecular information provides a depth of information that has the potential to revolutionize disease diagnosis, prediction of prognosis, and course of therapy.